Apparatus and method for  demand coordination network

ABSTRACT

An apparatus for controlling peak demand of a resource. The apparatus includes a plurality of devices and a plurality of control nodes. Each of the plurality of devices consumes a portion of the resource when turned on, and the each are capable of performing a corresponding function within an acceptable operational margin by cycling on and off. Each of the plurality of control nodes is coupled to a corresponding one of the plurality of devices, where the plurality of control nodes is coupled together via a demand coordination network, and where the plurality of control nodes coordinates run times for the each of the plurality of devices to reduce the peak demand of the resource, and where one or more of the run times start prior to when otherwise required to maintain corresponding local environments, but which still operate within the acceptable operating margin for the corresponding local environments.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following U.S. Provisional Application, which is herein incorporated by reference for all intents and purposes.

SERIAL FILING NUMBER DATE TITLE 61/303,160 Feb. 10, 2010 APPARATUS AND METHOD FOR (SF.0101) DISRUPTION TOLERANT DEMAND COORDINATION NETWORK

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates in general to the field of resource management, and more particularly to an apparatus and method for coordinating the use of certain resources such that a peak demand of those resources is optimized.

2. Description of the Related Art

The problem with resources such as electrical power, water, fossil fuels and their derivatives (e.g., natural gas) is that the generation and consumption of a resource both vary with respect to time. Further, the delivery and transport infrastructure limits instantaneous matching of generation and consumption. They are limited in supply and the demand for this limited supply is constantly fluctuating. As anyone who has participated in a rolling blackout will concur, the times are more and more frequent when resource consumers are forced to face the realities of limited resource supply.

Most notably, the electrical power generation and distribution community has begun to take proactive measures to protect limited instantaneous supplies of electrical power by imposing a demand charge on consumers in addition to their monthly usage charge. Heretofore, consumers merely paid for the total amount of power that they consumed over a billing period. Today most energy suppliers are not only charging customers for the total amount of electricity they have consumed over the billing period, but they are additionally charging them for their peak demand, that is the greatest amount of energy that they use during a measured period, typically on the order of minutes.

For example, consider a factory owner whose building includes 20 air conditioners, each consuming 10 KW when turned on. If they are all on at the same time, then the peak demand for that period is 200 KW. Not only does the energy supplier have to provide for instantaneous generation of this power in conjunction with loads exhibited by its other consumers, but the distribution network that supplies this peak power must be sized such that it delivers 200 KW.

So it is acceptable today that high peak demand consumers are required to pay a surcharge to offset the costs of peak energy generation and distribution. And the notion of peak demand charges, while presently are only levied to commercial electricity consumers and to selected residential consumers, is applicable to all residential consumers and consumers of other limited generation and distribution resources as well. Water and natural gas are prime examples of resources that will someday exhibit demand charges.

But consider in the facility example above that it is not time or comfort critical to run every air conditioning unit in the building at once. Run times can be staggered, perhaps, to mitigate peak demand. And this technique is what is presently employed in the industry to lower peak demand. There are very simply ways to stagger run times, and there are very complicated mechanisms that are employed to lower peak demand, but they all utilize variations of what is known in the art as deferral.

Stated simply, deferral means that some devices have to wait to run while other, perhaps higher priority, devices are allowed to run. Another form of deferral is to reduce the duty cycle (i.e., the percentage of the a device cycle that a device is on) of one or more devices in order to share the reduction in peak demand desired. What this means in the air conditioning example above is that some folks are going to be very uncomfortable while waiting for their turn to run, or that everyone in the facility is going to be mildly uncomfortable. And as one skilled in the art will appreciate, there is a zone of comfort beyond which productivity falls.

Virtually every system of resource consuming devices exhibits a margin of acceptable operation (“comfort zone” in the air conditioning example above) around which operation of the device in terms of start time, duration, and duty cycle can be deferred. But the present inventors have observed that present day techniques for controlling peak demand all involve delaying (“deferring”) the start times and durations of devices and decreasing the duty cycles, thus in many instances causing local environments to operate outside of their acceptable operational margins. It is either too hot, too cold, not enough water, the motors are not running long enough to get the job done, and etc.

Accordingly, what is needed is an apparatus and method for managing peak demand of a resource that considers acceptable operational margins in determining when and how long individual devices in a system will run.

What is also needed is a technique for scheduling run times for devices in a controlled system that is capable of advancing the start times and durations of those devices, and that is capable of increasing the duty cycles associated therewith in order to reduce demand while concurrently maintaining operation within acceptable operational margins.

What is additionally needed is a mechanism for modeling and coordinating the operation of a plurality of devices in order to reduce peak demand of a resource, where both advancement and deferral are employed effectively to reduce demand and retain acceptable operational performance.

What is moreover needed is an demand coordination apparatus and method that employs adaptive modeling of local environments and anticipatory scheduling of run times in order to reduce peak demand while maintaining acceptable operation.

Furthermore, what is needed is a demand coordination mechanism that will perform reliably and deterministically in the presence of periodic network disruptions.

SUMMARY OF THE INVENTION

The present invention, among other applications, is directed to solving the above-noted problems and addresses other problems, disadvantages, and limitations of the prior art. The present invention provides a superior technique for managing and controlling the demand level of a given resource as that resource is consumed by a plurality of consuming devices. In one embodiment, an apparatus for controlling peak demand of a resource is provided. The apparatus includes a plurality of devices and a plurality of control nodes. Each of the plurality of devices consumes a portion of the resource when turned on, and the each are capable of performing a corresponding function within an acceptable operational margin by cycling on and off. Each of the plurality of control nodes is coupled to a corresponding one of the plurality of devices, where the plurality of control nodes is coupled together via a demand coordination network, and where the plurality of control nodes coordinates run times for the each of the plurality of devices to reduce the peak demand of the resource, and where one or more of the run times start prior to when otherwise required to maintain corresponding local environments, but which still operate within the acceptable operating margin for the corresponding local environments.

One aspect of the present invention contemplates a peak demand control system, for managing peak energy demand. The peak demand control system includes a plurality of control nodes and one or more sensor nodes. Each of the plurality of control nodes is coupled to a corresponding one of a plurality of devices, where the plurality of control nodes are coupled together via a demand coordination network, and where the plurality of control nodes coordinates run times for the each of the plurality of devices to reduce the peak energy demand, and where one or more of the run times start prior to when otherwise required to maintain corresponding local environments, and where advancement of the one or more start times allows for operation of a corresponding one or more of the plurality of devices within an acceptable operating margin. The one or more sensor nodes are coupled to the demand coordination network, and are configured to provide one or more global sensor data sets to the plurality of control nodes, where the plurality of control nodes employs the one or more global sensor data sets in determining the run times.

Another aspect of the present invention comprehends a method for controlling peak demand of a resource. The method includes controlling devices via corresponding control nodes, where each of the devices consumes a portion of the resource when turned on, and where the each of the devices is capable of performing a corresponding function within an acceptable operational margin by cycling on and off; and first coupling the control nodes together via a demand coordination network, and employing the demand coordination network to coordinate run times for the each of the devices to reduce the peak demand of the resource, where one or more of the run times start prior to when otherwise required to maintain corresponding local environments, but which still operate within the acceptable operating margin for the corresponding local environments.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features, and advantages of the present invention will become better understood with regard to the following description, and accompanying drawings where:

FIG. 1 is a block diagram illustrating a demand coordination system according to the present invention;

FIG. 2 is a block diagram depicting a control node according to the present invention;

FIG. 3 is a block diagram featuring a local model module according to the present invention, such as might be disposed within the control node of FIG. 2;

FIG. 4 is a timing diagram showing an exemplary local model estimation performed by the local model module of FIG. 3;

FIG. 5 is a block diagram illustrating a global model module according to the present invention, such as might be disposed within the control node of FIG. 2;

FIG. 6 is a block diagram detailing a global schedule module according to the present invention, such as might be disposed within the control node of FIG. 2; and

FIG. 7 is a block diagram showing a local schedule module according to the present invention, such as might be disposed within the control node of FIG. 2.

DETAILED DESCRIPTION

The following description is presented to enable one of ordinary skill in the art to make and use the present invention as provided within the context of a particular application and its requirements. Various modifications to the preferred embodiment will, however, be apparent to one skilled in the art, and the general principles defined herein may be applied to other embodiments. Therefore, the present invention is not intended to be limited to the particular embodiments shown and described herein, but is to be accorded the widest scope consistent with the principles and novel features herein disclosed.

In view of the above background discussion on resource and energy demand and associated techniques employed within systems to control peak demand, a discussion of the present invention will now be presented with reference to FIGS. 1-7. The present invention provides for more flexible and optimal management and control of resource consumption, such as electrical energy, by enabling use of particular resources to be coordinated among resource consuming devices. In stark contrast to prior art mechanisms, the present invention employs scheduling techniques that allow for advancement, or preemptive cycling of devices, as well as deferral.

Referring to FIG. 1, a block diagram is presented illustrating a demand coordination system 100 according to the present invention. The system 100 includes a plurality of system devices 101, each of which is managed and controlled by the system 100 for purposes of consumption control in order to manage peak resource demand. In one embodiment, the system devices 101 comprise air-conditioning units that are disposed within a building or other facility and the resource that is managed comprises electrical power. In another embodiment, the system devices 101 comprise heating units that are disposed within a building or other facility and the resource that is managed comprises natural gas. The present inventors specifically note that the system 100 contemplated herein is intended to be preferably employed to control any type of resource consuming device 101 such as the units noted above, and also including, but not limited to, water pumps, heat exchangers, motors, generators, light fixtures, electrical outlets, sump pumps, furnaces, or any other device that is capable of being duty-cycle actuated in order to reduce peak demand of a resource, but which is also capable, in one embodiment, of maintaining a desired level of performance (“comfort level”) by advancing or deferring actuation times and increasing or decreasing duty cycles in coordination with other substantially similar devices 101. The present inventors also note that the present invention comprehends any form of consumable resource including, but not limited to, electrical power, natural gas, fossil fuels, water, and nuclear power. As noted above, present day mechanisms are in place by energy suppliers to levy peak demand charges for the consumption of electrical power by a consumer and, going forward, examples are discussed in terms relative to the supply, consumption, and demand coordination of electrical power for purposes only of teaching the present invention in well-known subject contexts, but it noted that any of the examples discussed herein may be also embodied to employ alternative devices 101 and resources discussed above for the coordination of peak demand of those resources within a system 100. It is also noted that the term “facility” is not to be restricted to construe a brick and mortar structure, but may also comprehend any form of interrelated system 100 of devices 101 whose performance can be modeled and whose actuations can be scheduled and controlled in order to coordinate demand of a particular resource.

Having noted the above, each of the devices 101 includes a device control 102 that operates to turn the device 101 on, thus consuming a resource, and off, thus not consuming the resource. When the device 101 is off, none of the resource is consumed, and thus a device that is off does not contribute to overall cumulative peak resource demand. The device control 102 does not have to be disposed within the device 101, nor does the device control 102 have to be collocated with the device 101.

A control node 103 according to the present invention is coupled to each of the device controls 102 via a device sense bus DSB 111 that is employed by the control node 103 to turn the device 101 on and off, to sense when the device 101 is turned on and off, and to further transparently enable the device 101 to operate independent of the demand coordination system 100 in a fail safe mode while at the same time sensing when the device 101 is turned on and turned off in the fail safe mode. Each of the control nodes 103 maintain control of their respective device 101 and in addition maintain a replicated copy of a global model of a system environment along with a global schedule for actuation of all of the devices 101 in the system 100. Updates to the global model and schedule, along with various sensor, monitor, gateway, configuration, and status messages are broadcast over a demand coordination network (DCN) 110, which interconnects all of the control nodes 103, and couples these control nodes to optional global sensor nodes 106, optional monitor nodes 109, and an optional gateway node 120. In one embodiment, the DCN 110 comprises an IEEE 802.15.4 packetized wireless data network as is well understood by those skilled in the art. Alternatively, the DCN 110 is embodied as an IEEE 802.11 packetized wireless or wired network. In another embodiment, the DCN 110 comprises a power line modulated network comporting with HOMEPLUG® protocol standards. Other packetized network configurations are additionally contemplated. The present inventors note, however, that the present invention is distinguished from conventional “state machine” techniques for resource demand management and control in that only model updates and schedule updates are broadcast over the DCN 110, thus providing a strong advantage according to the present invention in light of network disruption. For the 802.15.4 embodiment, replicated model and schedule copies on each control node 103 along with model and schedule update broadcasts according to the present invention are very advantageous in the presence of noise and multipath scenarios commonly experienced by wireless packetized networks. That is, a duplicate model update message that may be received by one or more nodes 103 does not serve to perturb or otherwise alter effective operation of the system 100.

Zero or more local sensors 104 are coupled to each of the control nodes 103 via a local sensor bus 112, and configuration of each of the local sensors 104 may be different for each one of the devices 101. Examples of local sensors 104 include temperature sensors, flow sensors, light sensors, and other sensor types that may be employed by the control node 103 to determine and model an environment that is local to a particular system device 101. For instance, a temperature sensor 104 may be employed by a control node 103 to sense the temperature local to a particular device 101 disposed as an air-conditioning unit. Another unit may employ local sensors 104 comprising both a temperature and humidity sensor local to a device 101 disposed as an air-conditioning unit. Other examples abound. Other embodiments contemplate collocation of local sensors 104 and device control 102 for a device 101, such as the well-known thermostat.

The system 100 also optionally includes one or more global sensors 105, each of which is coupled to one or more sensor nodes 106 according to the present invention. The sensors 105 may comprise, but are not limited to, occupancy sensors (i.e., movement sensors), solar radiation sensors, wind sensors, precipitation sensors, humidity sensors, temperature sensors, power meters, and the like. The sensors 105 are such that their data is employed to globally affect all modeled environments and schedules. For example, the amount of solar radiation on the facility has an impact to each local environment associated with each of the system devices 101, and therefore must be taken into account when developing a global model of the system environment. In one embodiment, the global model of the system environment is an aggregate of all local models associated with each of the devices, where each of the local models are adjusted based upon the data provided by the global sensors 105.

Each of the global sensors 105 is coupled to a respective sensor node 106 according to the present invention via a global sensor bus (GSB) 113, and each of the sensor nodes 106 are coupled to the DCN 110. Operationally, the sensor nodes 106 are configured to sample their respective global sensor 105 and broadcast changes to the sensor data over the DCN 110 to the control nodes 110 and optionally to the gateway node 120.

The system 100 also optionally includes one or more non-system devices 107, each having associated device control 108 that is coupled to a respective monitor node 109 via a non-system bus (NSB) 114. Each of the monitor nodes 109 is coupled to the DCN 110. Operationally, each monitor node 109 monitors the state of its respective non-system device 107 via its device control 108 to determine whether the non-system device 107 is consuming the managed resource (i.e., turned on) or not (i.e., turned off). Changes to the status of each non-system device 107 is broadcast by its respective monitor node 109 over the DCN 110 to the control nodes 103 and optionally to the gateway node 120. The non-system devices 107 may comprise any type of device that consumes the resource being managed, but which is not controlled by the system 100. One example of such a non-system device 107 is an elevator in a building. The elevator consumes electrical power, but may not be controlled by the system 100 in order to reduce peak demand. Thus, in one embodiment, consumption of the resource by these non-system devices 107 is employed as a factor during scheduling of the system devices 101 in order to manage and control peak demand of the resource.

Optionally, the gateway node 120 is coupled by any known means to a network operations center (NOC) 121. In operation, configuration data for the system 100 may be provided by the NOC 121 and communicated to the gateway node 120. Alternatively, configuration data may be provided via the gateway node 120 itself. Typically, the gateway node 120 is collocated with the system 100 whereas the NOC 121 is not collocated and the NOC 121 may be employed to provide configuration data to a plurality of gateway nodes 120 corresponding to a plurality of systems 100. The configuration data may comprise, but is not limited to, device control data such as number of simultaneous devices in operation, device operational priority relative to other devices, percentage of peak load to employ, peak demand profiles related to time of day, and the like.

Thus, as will be described in more detail below, each of the control nodes 103 develops a local environment model that is determined from corresponding local sensors 104. Each local environment model, as changes to the local environment model occur, is broadcast over the DCN 110 to all other control nodes 103. Each of the control nodes 103 thus maintains a global environmental model of the system 100 which, in one embodiment, comprises an aggregation of all of the local environmental models. Each of the global models is modified to incorporate the effect of data provided by the global sensors 105. Thus, each identical global model comprises a plurality of local environmental models, each of which has been modified due to the effect of data provided by the global sensors 105. It is important to note that the term “environmental” is intended to connote a modeling environment as opposed to a physical environment.

Each control node 103, as will be described below, additionally comprises a global schedule which, like the global model, is an aggregate of a plurality of local run time schedules, each associated with a corresponding device 101. The global schedule utilizes the global model data in conjunction with configuration data and data provided by the monitor nodes 109, to develop the plurality of local run time schedules, where relative start times, duration times, and duty cycle times are established such that comfort margins associated with each of the local environments are maintained, in one embodiment, via maintaining, advancing (i.e., running early), or deferring (i.e., delaying) their respective start times and durations, and via maintaining, advancing, or deferring their respective duty cycles.

Turning now to FIG. 2, a block diagram is presented depicting a control node 200 according to the present invention. The control node 200 includes a node processor 201 that is coupled to one or more local sensors (not shown) via a local sensor bus (LSB) 202, a device control (not shown) via a device sense bus (DSB) 203, and to a demand coordination network (DCN) 204 as has been described above with reference to FIG. 1.

The control node 200 also includes a local model module 204 that is coupled to the node processor 201 via a synchronization bus (SYNC) 209, a sensor data bus (SENSEDATA) 215, and a device data bus (DEVDATA) 216. The control node 200 also has a global model module 206 that is coupled to the node processor 201 via SYNC 209 and via an inter-node messaging bus (INM) 211. The global model module 206 is coupled to the local model module 205 via a local model environment bus (LME) 212. The control node 200 further includes a global schedule module 207 that is coupled to the node processor 201 via SYNC 209 and INM 211, and that is coupled to the global model module 206 via a global relative run environment bus (GRRE) 213. The control node finally includes a local schedule module 208 that is coupled to the node processor 201 via SYNC 209 and a run control bus (RUN CTRL) 210. The local schedule module 208 is also coupled to the global schedule module 207 via a local relative run environment bus (LRRE) 214. LRRE 214 is also coupled to the global model module 206. In addition, a run time feedback bus (RTFB) 217 couples the local schedule module 208 to the local model module 205.

The node processor 201, local model module 205, global model module 206, global schedule model 207, and local schedule model 208 according to the present invention are configured to perform the operations and functions as will be described in further detail below. The node processor 201 local model module 205, global model module 206, global schedule model 207, and local schedule model 208 each comprises logic, circuits, devices, or microcode (i.e., micro instructions or native instructions), or a combination of logic, circuits, devices, or microcode, or equivalent elements that are employed to perform the operations and functions described below. The elements employed to perform these operations and functions may be shared with other circuits, microcode, etc., that are employed to perform other functions within the control node 200. According to the scope of the present application, microcode is a term employed to refer to one or more micro instructions.

In operation, synchronization information is received by the node processor 201. In one embodiment, the synchronization information is time of day data that is broadcast over the DCN 204. In an alternative embodiment, a synchronization data receiver (not shown) is disposed within the node processor 201 itself and the synchronization data includes, but is not limited to, atomic clock broadcasts, a receivable periodic synchronization pulse such as an amplitude modulated electromagnetic pulse, and the like. The node processor 201 is further configured to determine and track relative time for purposes of tagging events and the like based upon reception of the synchronization data. Preferably, time of day is employed, but such is not necessary for operation of the system.

The node processor 201 provides periodic synchronization data via SYNC 209 to each of the modules 205-208 to enable the modules 205-208 to coordinate operation and to mark input and output data accordingly. The node processor 201 also periodically monitors data provided by the local sensors via LSB 202 and provides this data to the local model module 205 via SENSEDATA 215. The node processor 201 also monitors the DSB 203 to determine when an associated device (not shown) is turned on or turned off. Device status is provided to the local model module 205 via DEVDATA. The node processor 201 also controls the associated device via the DSB 203 as is directed via commands over bus RUN CTRL 210. The node processor further transmits and receives network messages over the DCN 204. Received message data is provided to the global model module 206 or the global schedule model 207 as appropriate over bus INM 211. Likewise, both the global model module 206 and the global schedule model 207 may initiate DCN messages via commands over bus INM 211. These DCN messages primarily include, but are not limited to, broadcasts of global model updates and global schedule updates. System configuration message data as described above is distributed via INM 211 to the global schedule module 207.

Periodically, in coordination with data provide via SYNC 209, the local model module employs sensor data provided via SENSEDATA 215 in conjunction with device actuation data provided via DEVDATA 216 to develop, refine, and update a local environmental model which comprises, in one embodiment, a set of descriptors that describe a relative time dependent flow of the local environment as a function of when the associated device is on or off. For example, if the device is an air conditioning unit and the local sensors comprise a temperature sensor, then the local model module 205 develops, refines, and updates a set of descriptors that describe a local temperature environment as a relative time function of the data provided via SYNC 209, and furthermore as a function of when the device is scheduled to run and the parameters associated with the scheduled run, which are received from the local schedule module 208 via RTFB 217. This set of descriptors is provided to the global model module 206 via LME 212. However, it is noted that these descriptors are updated and provided to LME 212 only when one or more of the descriptors change to the extent that an error term within the local model module 205 is exceeded. In addition to the descriptors, data provided on LME 212 by the local model module includes an indication of whether the descriptors accurately reflect the actual local environment, that is, whether the modeled local environment is within an acceptable error margin when compared to the actual local environment. When the modeled local environment exceeds the acceptable error margin when compared to the actual local environment, then the local model module 205 indicates that its local environment model is inaccurate over LME 212, and the system may determine to allow the associated device to run under its own control in a fail safe mode. For instance, if occupancy of a given local area remains consistent, then a very accurate model of the local environment will be developed over a period of time, and updates of the descriptors 212 will decrease in frequency, thus providing advantages when the DCN 204 is disrupted. It is noted that the error term will decrease substantially in this case. However, consider a stable local environment model that is continually perturbed by events that cannot be accounted for in the model, such as impromptu gatherings of many people. In such a case the error term will be exceeded, thus causing the local model module 205 to indicate over LME 212 that its local environment model is inaccurate. In the case of a system comprising air conditioning units, it may be determined to allow the associated unit to run in fail safe mode, that is, under control of its local thermostat. Yet, advantageously, because all devices continue to use their replicated copies of global models and global schedules, the devices continue to operate satisfactorily in the presences of disruption and network failure for an extended period of time. Additionally, if model error over time is known, then all devices in the network can utilize pre-configured coordination schedules, effectively continuing coordination over an extended period of time, in excess of the models ability to stay within a known margin of error. Furthermore, it can be envisioned that devices without a DCN, utilizing some externally sensible synchronization event, and with known model environments, could perform coordination sans DCN.

The local model module 205, in addition to determining the above noted descriptors, also maintains values reflecting accuracy of the local sensors, such as hysteresis of a local thermostat, and accounts for such in determining the descriptors. Furthermore, the local model module 205 maintains and communicates via LME 212 acceptable operation margin data to allow for advancement or deferral of start times and durations, and increase or decrease of duty cycles. In an air conditioning or heating environment, the acceptable operation margin data may comprise an upper and lower temperature limit that is outside of the hysteresis of the local temperature sensor, but that is still acceptable from a human factors perspective in that it is not noticeable to a typical person, thus not adversely impacting that person's productivity.

In one embodiment, the descriptors comprise one or more coefficients and an offset associated with a linear device on equation and one or more coefficients and intercept associated with a linear device off equation. Other equation types are contemplated as well to include second order equations, complex coefficients, or lookup tables in the absence of equation-based models. What is significant is that the local model module generates and maintains an acceptable description of its local environment that is relative to a synchronization event such that the global model module 206 can predict the local environment as seen by the local model module.

The global model module 206 receives the local descriptors via LME 212 and stores this data, along with all other environments that are broadcast over the DCN and received via the INM 211. In addition, the global model module adjusts its corresponding local environment entry to take into account sensor data from global sensors (e.g., occupancy sensors, solar radiation sensors) which is received over the DCN 204 and provided via the INM 211. An updated local entry in the global model module 206 is thus broadcast over the DCN 204 to all other control nodes in the system and is additionally fed back to the local model module to enable the local model module to adjust its local model to account for the presence of global sensor data.

The global model module 206 provides all global model entries to the global schedule module 207 via GRRE 213. The global schedule module 207 employs these models to determine when and how long to actuate each of the devices in the system. In developing a global device schedule, the global schedule module utilizes the data provided via GRRE 213, that is, aggregate adjusted local models for the system, along with system configuration data as described above which is resident at installation or which is provided via a broadcast over the DCN 204 (i.e., a NOC-initiated message over the gateway node). The global device actuation schedule a schedule of operation relative to the synchronization event and is broadcast over the DCN 204 to all other control nodes. In addition, the device actuation schedule associated with the specific control node 200 is provided over LRRE 214 to both the local schedule module 208 and the local model module, for this data directs if and when the device associated with the specific control node 200 will run. It is noted that the global schedule module 207 operates substantially to reduce peak demand of the system by advancing or deferring device start times and increasing or decreasing device duty cycles in accordance with device priorities. The value by which a time is advanced or deferred and the amount of increase or decrease to a duty cycle is determined by the global schedule module 207 such that higher priority devices are not allowed to operate outside of their configured operational margin. In addition, priorities, in one embodiment, are dynamically assigned by the global schedule module 207 based upon the effect of the device's timing when turned on. Other mechanisms are contemplated as well for dynamically assigning device priority within the system.

The local schedule module 208 directs the associated device to turn on and turn off at the appropriate time via commands over RUN CTRL 210, which are processed by the node processor 201 and provided to the device control via DSB 203.

Now referring to FIG. 3, a block diagram is presented featuring a local model module 300 according to the present invention, such as might be disposed within the control node 200 of FIG. 2. As is described above with reference to FIG. 2, the local model module 300 performs the function of developing, updating, and maintaining an acceptably accurate model of the local environment. Accordingly, the local model module 300 includes a local data processor 301 that is coupled to busses SENSEDATA, DEVDATA, SYNC, and RTFB. Data associated with the local environment is stamped relative to the synchronization data provided via SYNC and entries are provided to a local data array 302 via a tagged entry bus TAGGED ENTRY. The local model module 300 also includes a local model estimator 303 that is coupled to the local data array 302 and which reads the tagged entries and develops the descriptors for the local environment when the device is on an when the device is off, as described above. The local model estimator 303 include an initiation processor 304 that is coupled to an LME interface 306 via bus ONLINE and an update processor 305 that is coupled to the LME interface 306 via bus NEW. The LME interface 306 generates data for the LME bus.

In operation, the local data processor 301 monitors SENSEDATA, DEVDATA, and RTFB. If data on any of the busses changes, then the local data processor 301 creates a tagged entry utilizing time relative to data provided via SYNC and places the new tagged entry into the local data array 302. Periodically, the local model estimator 303 examines the entries in the local data array 302 and develops the descriptors described above. The period at which this operation is performed is a function of the type of devices in the system. In one embodiment, development of local environment model descriptors is performed at intervals ranging from 1 second to 10 minutes, although one skilled in the art will appreciate that determination of a specific evaluation interval time is a function of device type, number of devices, and surrounding environment. The update processor 305 monitors successive evaluations to determine if the value of one or more of the descriptors changes as a result of the evaluation. If so, then the update processor 305 provides the new set of descriptors to the LME interface 306 via bus NEW.

The initialization processor 304 monitors the accuracy of the modeled local environment as compared to the real local environment. If the accuracy exceeds and acceptable error margin, then the initialization processor 304 indicates such via bus ONLINE and the LME interface 306 reports this event to the global model module (not shown) via bus LME. As a result, the local device may be directed to operate in fail safe mode subject to constraints and configuration data considered by the global schedule module (not shown). Advantageously, the initialization processor 304 enables a control node according to the present invention to be placed in service without any specific installation steps. That is, the control node is self-installing. In one embodiment, as the local model module learns of the local environment, the initialization processor 304 indicates that the error margin is exceeded and as a result the local device will be operated in fail safe mode, that is, it will not be demand controlled by the system. And when development of the local model falls within the error margin, the initialization processor 304 will indicate such and the local device will be placed online and its start times and durations will be accordingly advanced or deferred and its duty cycle will be increased or decreased, in conjunction with other system devices to achieve the desired level of peak demand control.

Turning to FIG. 4, a timing diagram 400 is presented showing an exemplary local model estimation performed by the local model module of FIG. 3. The diagram 400 includes two sections: a parameter estimation section 401 and a device state section 402. The parameter estimation section 401 shows a setpoint for the device along with upper and lower hysteresis values. In some devices, hysteresis is related to the accuracy of the local sensor. In other devices, hysteresis is purposely built in to preclude power cycling, throttling, oscillation, and the like. In a cooling or heating unit, the hysteresis determines how often the device will run and for how long. The parameter estimation section 401 also shows an upper operational margin and a lower operational margin, outside of which the local device is not desired to operate. The parameter estimation section 401 depicts an estimated device off line (UP) 403 that is the result of applying estimated descriptors over time for when the device is turned off, and an estimated device on line (DN) 404 that is the result of applying estimated descriptors over time for when the device is turned on. One area of demand control where this example is applicable is for a local air conditioning unit that is controlled by a local thermostat. Accordingly, the local data processor 301 provides tagged entries to the local data array 302 as noted above. Device status (on or off) is provided either directly from DEVDATA bus or indirectly from RTFB (if DEVDATA is incapable of determining on and off state). The entries corresponding to each of the two states are evaluated and a set of descriptors (i.e., parameters) are developed that describe the local environment. In one embodiment, a linear fit algorithm is employed for the on time and off time of the device. By using device status 405, the local model estimator 303 can determine descriptors for UP 403, DN 404, and the upper and lower hysteresis levels. Upper and lower margin levels are typically provided by configuration. In the air conditioning example, the parameter being estimated is local temperature and thus the upper and lower margins would vary perhaps two degrees above and below the hysteresis levels. Note that prior to time T1, the device is off and the parameter, as indicated by local sensor data, is increasing. At time T1 the device turns on, subsequently decreasing the parameter. At time T2, the device turns off and the parameter begins increasing in value. At time T3 the device turns on again and the parameter decreases. At time T4, the device turns off and the parameter increases.

By determining the descriptors and knowing the upper and lower margins, a global scheduler is enabled to determine how long it can advance (point TA) or delay (TD) a start time, for example. In addition, the descriptors developed by the local model for the operational curves 403, 404, as adjusted by the global model module, enable a global scheduler to advance or defer start and/or duration, or increase or decrease duty cycle of the device in a subsequent cycle in order to achieve the desired peak demand control while maintaining operation of the device within the upper and lower margin boundaries. Advantageously, the model according to the present invention is configured to allow for estimation of the precise position in time of the device on the curves 403, 404, which enables, among other features, the ability of the system to perform dynamic hysteresis modification, or overriding intrinsic hysteresis of a device. In addition, the initialization processor 304 can monitor the actual environment from local sensor data and compare it to the curves 403, 404 to determine if and when to place the device online for demand control. The descriptors that describe the UP segment 403 and DN segment 404 are communicated to the global model module via bus LME.

Now referring to FIG. 5, a block diagram illustrating a global model module 500 according to the present invention, such as might be disposed within the control node 200 of FIG. 2. As is noted in the discussion with reference to FIG. 2, the global model module 500 performs two functions. First, the global model module 500 adjusts the descriptors associated with the local environment as provided over bus LME to account for global sensor data provided via messages broadcast for global sensor nodes over the demand control network. Secondly, the global model module stores replica copies of all other local environment descriptors in the system, as each of those local environment descriptors have been adjusted by their respective global model modules.

The global model module 500 includes a global data processor 501 that receives local descriptors and other data via bus LME from its corresponding local model module. In addition, the global data processor 501 interfaces to busses INM, SYNC, and LRRE to receive/transmit data as described above. Local descriptors are stamped and entered into a global data array 502 via bus LME entry. The remaining adjusted local descriptors from other devices are received via bus INM and are entered into the global data array 502 via bus GLB entry.

A global model estimator 503 is coupled to the global data array 502 and to the global data processor 501 via busses GLB SENSOR DATA, ACTUAL LOCAL RUN DATA, and UPDATE MESSAGE DATA. Global sensor data that is received over INM is provided to the estimator 503 via GLB SENSOR DATA. Actual run time data for the corresponding local device that is received over bus LRRE is provided to the estimator 503 via ACTUAL LOCAL RUN DATA.

In operation, the global model estimator 503 retrieves its corresponding local environment descriptor entry from the global data array 502. The global model estimator 503 includes an environment updater 504 that modifies the local descriptor retrieved from the array to incorporate the effects of global sensor data provided over GLB SENSOR DATA. For example, the value of an external building temperature sensor is a parameter that would affect every local temperature descriptor set in the system. The environment updater 504 modifies its local descriptor set to incorporate any required changes due to global sensor values. In addition, the environment updater 504 employs the actual run data of the associated device to enable it to precisely determine at what point on the estimated local environmental curve that it is at when modifying the local descriptors.

If the environment updater 504 modifies a local descriptor set, its corresponding entry in the array 502 is updated and is provided to a messaging interface 506 and to a GRRE interface. The messaging interface 506 configures update message data and provides this data via UPDATE MESSAGE DATA to the processor 501 for subsequent transmission over the DCN. The GRRE interface 505 provides the updated local environment descriptor set to bus GRRE. All operations are performed relative to synchronization event data provided via SYNC.

Turning to FIG. 6, a block diagram is presented detailing a global schedule module 600 according to the present invention, such as might be disposed within the control node 200 of FIG. 2. As described above, the global schedule module 600 is responsible for determining a schedule of operation (turn on, duration, and duty cycle) for each of the devices in the system. When the local environment descriptors are updated by a coupled global model module and are received over bus GRRE, then the global schedule module 600 operates to revise the global schedule of device operation and to broadcast this updated schedule over the DCN.

The global schedule module 600 includes a global data processor 601 that interfaces to INM for reception/transmission of DCN related data, bus GRRE for reception of updated local environment descriptors, and bus SYNC for reception of synchronization event data. DCN data that is provided to the global schedule module 600 includes broadcast global schedules from other control nodes, and non-system device data and configuration data as described above. The global data processor 601 provides updated global schedule data, received over the DCN from the other control nodes, to a global schedule array 602 via bus GLB ENTRY. The global processor 601 is coupled to a global scheduler 603 via bus NON-SYSTEM/CONFIG DATA for transmittal of the non-system device data and configuration data. The global processor 601 is also coupled to the global scheduler 603 via bus GRRE data for transmittal of updated local environment descriptors provided via bus GRRE. And the global scheduler 603 is coupled to the processor 601 via bus UPDATE MESSAGE DATA to provide INM data resulting in DCN messages that broadcast an updated global schedule generated by this module 600 to other control nodes in the system.

The global scheduler 603 includes a demand manager 604 that is coupled to an LRRE interface 605 via bus LOC and to a messaging interface 606 via bus UPDATE. When data is received over either the NON-SYSTEM/CONFIG DATA bus or the GRRE data bus, the demand manager recalculates a global relative run schedule for all devices in the system. The schedule for an individual device includes, but is not limited to, a relative start time, a duration, and a duty cycle. The relative start time and/or duration may be advanced, maintained, or deferred in order to achieve configured constraints of the system in conjunction with the operation of non-system devices and the amount of resource that they consume. In addition, for similar purposes the duty cycle for each device in the system may be increased or decreased. Yet, as one skilled will appreciate, the system accounts for limits to devices duty cycle modification to prevent unintended damage to a device. The result is an updated global schedule, which is stored in the array 602, and which is broadcast via update messages over the DCN provided via bus UPDATE. In addition, the relative run schedule for the corresponding local device is provided via bus LOC to the LRRE interface 605, and which is placed on bus LRRE for transmission to a corresponding local schedule module.

FIG. 7 is a block diagram showing a local schedule module 700 according to the present invention, such as might be disposed within the control node 200 of FIG. 2. The local schedule module 700 includes a local schedule processor 701 that is coupled to bus RUN CTRL, bus LRRE, bus SYNC, and bus RTFB. The local schedule processor 701 includes a real-time converter 702 and a fail safe manager 703.

In operation, the local schedule processor 701 receives an updated local run schedule for its associated device. The real-time converter establishes an actual run time for the device based upon the synchronization data provided via SYNC and the relative run time data received over LRRE. This real-time data is provided to a corresponding local model module via bus RTFB to enable the local model module to establish device on/off times in the absence of the device's ability to provide that data itself. Accordingly, the processor 701 directs the device to turn on and turn off via commands over RUN CTRL in comport with the actual run time schedule. In the event that the LRRE includes an indication that the local model is not within an acceptable error range, as described above, the fail safe manager 703 directs the device via RUN CTRL to operate independently.

Although the present invention and its objects, features, and advantages have been described in detail, other embodiments are encompassed by the invention as well. For example, the present invention has been primarily described herein as being useful for managing consumption side peak demand. However, the scope of the present invention extends to a system of devices (e.g., generators) on the supply side for controlling the supply of a resource. Such is an extremely useful application that is contemplated for supply of a resource by a resource supplier having numerous, but not simultaneously operable supply devices. One such example is a state-wide electrical supply grid.

In addition, the present invention comprehends geographically distributed systems as well to include a fleet of vehicles or any other form of system whose local environments can be modeled and associated devices controlled to reduce peak demand of a resource.

Moreover, the present invention contemplates devices that comprise variable stages of consumption rather than the simple on/off stages discussed above. In such configurations, a control node according to the present invention is configured to monitor, model, and control a variable stage consumption device.

Those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention, and that various changes, substitutions and alterations can be made herein without departing from the scope of the invention as defined by the appended claims. 

1. An apparatus for controlling peak demand of a resource, the apparatus comprising: a plurality of devices, each consuming a portion of the resource when turned on, and said each capable of performing a corresponding function within an acceptable operational margin by cycling on and off; and a plurality of control nodes, each coupled to a corresponding one of said plurality of devices, wherein said plurality of control nodes is coupled together via a demand coordination network, and wherein said plurality of control nodes coordinates run times for said each of said plurality of devices to reduce the peak demand of the resource, and wherein one or more of said run times start prior to when otherwise required to maintain corresponding local environments, but which still operate within said acceptable operating margin for said corresponding local environments.
 2. The apparatus as recited in claim 1, wherein coordination of said run times for each of said plurality of devices comprises advancing a first one or more of a plurality of start times, deferring a second one or more of said plurality of start times, and increasing one or more of a plurality of duty cycles.
 3. The apparatus as recited in claim 1, wherein said each of said plurality of control nodes maintains a replica copy of a global run time schedule for all of said plurality of devices.
 4. The apparatus as recited in claim 1, wherein said each of said plurality of control nodes develops a set of descriptors characterizing an associated one of said corresponding local environments.
 5. The apparatus as recited in claim 4, wherein one of said plurality of control nodes employs additional sensor data to adjust an associated set of descriptors.
 6. The apparatus as recited in claim 1, wherein said each of said plurality of control nodes develops and maintains a replica copy of a global run time schedule that specifies start time, duration, and duty cycle for said each of said plurality of devices.
 7. The apparatus as recited in claim 6, further comprising: a monitor node, coupled to non-system device and to said demand coordination network, configured to broadcast when said non-system device is consuming the resource, wherein said each of said plurality of control nodes accounts for consumption of the resource by said non-system device when generating said global run time schedule.
 8. The apparatus as recited in claim 1, wherein said demand coordination network comprises an IEEE 802.15.4 wireless network.
 9. A peak demand control system, for managing peak energy demand, the peak demand control system comprising: a plurality of control nodes, each coupled to a corresponding one of a plurality of devices, wherein said plurality of control nodes are coupled together via a demand coordination network, and wherein said plurality of control nodes coordinates run times for said each of said plurality of devices to reduce the peak energy demand, and wherein one or more of said run times start prior to when otherwise required to maintain corresponding local environments, and wherein advancement of said one or more start times allows for operation of a corresponding one or more of said plurality of devices within an acceptable operating margin; and one or more sensor nodes, coupled to said demand coordination network, configured to provide one or more global sensor data sets to said plurality of control nodes, wherein said plurality of control nodes employs said one or more global sensor data sets in determining said run times.
 10. The peak demand control system as recited in claim 9, wherein coordination of said run times for each of said plurality of devices comprises advancing a first one or more of a plurality of start times, deferring a second one or more of said plurality of start times, and increasing one or more of a plurality of duty cycles.
 11. The peak demand control system as recited in claim 9, wherein said each of said plurality of control nodes maintains a replica copy of a global run time schedule for all of said plurality of devices.
 12. The peak demand control system as recited in claim 9, wherein said each of said plurality of control nodes develops a set of descriptors characterizing an associated one of said corresponding local environments.
 13. The peak demand control system as recited in claim 12, wherein one of said plurality of control nodes employs said one or more global sensor data sets to adjust an associated set of descriptors.
 14. The peak demand control system as recited in claim 9, wherein said each of said plurality control nodes develops and maintains a replica copy of a global run time schedule that specifies start time, duration, and duty cycle for said each of said plurality of devices.
 15. The peak demand control system as recited in claim 14, further comprising: a monitor node, coupled to non-system device and to said demand coordination network, configured to broadcast when said non-system device is consuming energy, wherein said each of said plurality of control nodes accounts for consumption of said energy by said non-system device when generating said global run time schedule.
 16. The peak demand control system as recited in claim 9, wherein said demand coordination network comprises an IEEE 802.15.4 wireless network.
 17. A method for controlling peak demand of a resource, the method comprising: controlling devices via corresponding control nodes, where each of the devices consumes a portion of the resource when turned on, and where the each of the devices is capable of performing a corresponding function within an acceptable operational margin by cycling on and off; and first coupling the control nodes together via a demand coordination network, and employing the demand coordination network to coordinate run times for the each of the devices to reduce the peak demand of the resource, wherein one or more of the run times start prior to when otherwise required to maintain corresponding local environments, but which still operate within the acceptable operating margin for the corresponding local environments.
 18. The method as recited in claim 17, wherein coordination of the run times for each of the devices comprises advancing a first one or more of a plurality of start times, deferring a second one or more of the plurality of start times, and increasing one or more of a plurality of duty cycles.
 19. The method as recited in claim 17, wherein each of the corresponding control nodes maintains a replica copy of a global run time schedule for all of the devices.
 20. The method as recited in claim 17, wherein the each of the corresponding control nodes develops a set of descriptors characterizing an associated one of the corresponding local environments.
 21. The method as recited in claim 21, wherein one of the corresponding control nodes employs additional sensor data to adjust an associated set of descriptors.
 22. The method as recited in claim 17, wherein the each of the corresponding control nodes develops and maintains a replica copy of a global run time schedule that specifies start time, duration, and duty cycle for the each of the devices.
 23. The apparatus as recited in claim 22, further comprising: second coupling a non-system device and to the demand coordination network, and broadcasting when the non-system device is consuming the resource, wherein said each of the corresponding control nodes accounts for consumption of the resource by the non-system device when generating said global run time schedule. 